Overview

Dataset statistics

Number of variables91
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.9 KiB
Average record size in memory736.0 B

Variable types

Categorical76
Numeric15

Warnings

_STATE has constant value "1.0" Constant
QSTVER has constant value "10.0" Constant
QSTLANG has constant value "1.0" Constant
GENHLTH is highly correlated with _RFHLTHHigh correlation
EXERANY2 is highly correlated with _TOTINDAHigh correlation
CVDINFR4 is highly correlated with _MICHDHigh correlation
CVDCRHD4 is highly correlated with _MICHDHigh correlation
ASTHMA3 is highly correlated with _LTASTH1 and 2 other fieldsHigh correlation
HAVARTH3 is highly correlated with _DRDXAR1High correlation
LASTDEN4 is highly correlated with _DENVST3High correlation
EDUCA is highly correlated with _EDUCAGHigh correlation
EMPLOY1 is highly correlated with _HCVU651 and 4 other fieldsHigh correlation
CHILDREN is highly correlated with _CHLDCNTHigh correlation
INCOME2 is highly correlated with _INCOMGHigh correlation
DIFFWALK is highly correlated with DIFFALON and 1 other fieldsHigh correlation
DIFFALON is highly correlated with DIFFWALKHigh correlation
SMOKE100 is highly correlated with _SMOKER3High correlation
USENOW3 is highly correlated with _HISPANC and 1 other fieldsHigh correlation
ALCDAY5 is highly correlated with DRNKANY5 and 2 other fieldsHigh correlation
SEATBELT is highly correlated with HIVRISK5 and 2 other fieldsHigh correlation
HIVTST6 is highly correlated with HIVRISK5 and 1 other fieldsHigh correlation
HIVRISK5 is highly correlated with SEATBELT and 2 other fieldsHigh correlation
_METSTAT is highly correlated with _URBSTATHigh correlation
_URBSTAT is highly correlated with _METSTATHigh correlation
_IMPRACE is highly correlated with _PRACE1 and 6 other fieldsHigh correlation
_RFHLTH is highly correlated with GENHLTH and 1 other fieldsHigh correlation
_HCVU651 is highly correlated with EMPLOY1 and 4 other fieldsHigh correlation
_TOTINDA is highly correlated with EXERANY2High correlation
_MICHD is highly correlated with CVDINFR4 and 1 other fieldsHigh correlation
_LTASTH1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_CASTHM1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_ASTHMS1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_DRDXAR1 is highly correlated with HAVARTH3High correlation
_DENVST3 is highly correlated with LASTDEN4High correlation
_PRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_MRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_HISPANC is highly correlated with USENOW3 and 1 other fieldsHigh correlation
_RACE is highly correlated with _IMPRACE and 6 other fieldsHigh correlation
_RACEG21 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACEGR3 is highly correlated with USENOW3 and 8 other fieldsHigh correlation
_RACE_G1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_AGEG5YR is highly correlated with EMPLOY1 and 4 other fieldsHigh correlation
_AGE65YR is highly correlated with EMPLOY1 and 4 other fieldsHigh correlation
_AGE80 is highly correlated with EMPLOY1 and 5 other fieldsHigh correlation
_AGE_G is highly correlated with EMPLOY1 and 4 other fieldsHigh correlation
_BMI5 is highly correlated with _BMI5CAT and 1 other fieldsHigh correlation
_BMI5CAT is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_RFBMI5 is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_CHLDCNT is highly correlated with CHILDREN and 1 other fieldsHigh correlation
_EDUCAG is highly correlated with EDUCAHigh correlation
_INCOMG is highly correlated with INCOME2High correlation
_SMOKER3 is highly correlated with SMOKE100 and 1 other fieldsHigh correlation
_RFSMOK3 is highly correlated with _SMOKER3High correlation
DRNKANY5 is highly correlated with ALCDAY5 and 2 other fieldsHigh correlation
DROCDY3_ is highly correlated with ALCDAY5 and 2 other fieldsHigh correlation
_RFBING5 is highly correlated with _IMPRACE and 4 other fieldsHigh correlation
_DRNKWEK is highly correlated with _RFBING5 and 1 other fieldsHigh correlation
_RFDRHV6 is highly correlated with _RFBING5 and 1 other fieldsHigh correlation
_RFSEAT2 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_RFSEAT3 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_DRNKDRV is highly correlated with ALCDAY5 and 2 other fieldsHigh correlation
_AIDTST3 is highly correlated with HIVTST6 and 1 other fieldsHigh correlation
GENHLTH is highly correlated with _RFHLTHHigh correlation
PHYSHLTH is highly correlated with _PHYS14DHigh correlation
MENTHLTH is highly correlated with _MENT14DHigh correlation
EXERANY2 is highly correlated with _TOTINDAHigh correlation
CVDINFR4 is highly correlated with _MICHDHigh correlation
CVDCRHD4 is highly correlated with _MICHDHigh correlation
ASTHMA3 is highly correlated with _LTASTH1 and 2 other fieldsHigh correlation
HAVARTH3 is highly correlated with _DRDXAR1High correlation
LASTDEN4 is highly correlated with _DENVST3High correlation
RMVTETH4 is highly correlated with _EXTETH3High correlation
EDUCA is highly correlated with _EDUCAGHigh correlation
EMPLOY1 is highly correlated with _AGEG5YR and 1 other fieldsHigh correlation
CHILDREN is highly correlated with _CHLDCNTHigh correlation
INCOME2 is highly correlated with _INCOMGHigh correlation
DECIDE is highly correlated with DIFFALONHigh correlation
DIFFWALK is highly correlated with DIFFALON and 1 other fieldsHigh correlation
DIFFALON is highly correlated with DECIDE and 1 other fieldsHigh correlation
SMOKE100 is highly correlated with _SMOKER3High correlation
USENOW3 is highly correlated with _HISPANCHigh correlation
ALCDAY5 is highly correlated with DRNKANY5 and 3 other fieldsHigh correlation
PNEUVAC4 is highly correlated with _AGE_GHigh correlation
SEATBELT is highly correlated with _RFSEAT2 and 1 other fieldsHigh correlation
HIVTST6 is highly correlated with _AIDTST3High correlation
_METSTAT is highly correlated with _URBSTATHigh correlation
_URBSTAT is highly correlated with _METSTATHigh correlation
_IMPRACE is highly correlated with _PRACE1 and 5 other fieldsHigh correlation
_RFHLTH is highly correlated with GENHLTH and 1 other fieldsHigh correlation
_PHYS14D is highly correlated with PHYSHLTHHigh correlation
_MENT14D is highly correlated with MENTHLTHHigh correlation
_HCVU651 is highly correlated with _AGEG5YR and 3 other fieldsHigh correlation
_TOTINDA is highly correlated with EXERANY2High correlation
_MICHD is highly correlated with CVDINFR4 and 1 other fieldsHigh correlation
_LTASTH1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_CASTHM1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_ASTHMS1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_DRDXAR1 is highly correlated with HAVARTH3High correlation
_EXTETH3 is highly correlated with RMVTETH4High correlation
_DENVST3 is highly correlated with LASTDEN4High correlation
_PRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_MRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_HISPANC is highly correlated with USENOW3High correlation
_RACE is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACEG21 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACEGR3 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACE_G1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_AGEG5YR is highly correlated with EMPLOY1 and 4 other fieldsHigh correlation
_AGE65YR is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_AGE80 is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_AGE_G is highly correlated with EMPLOY1 and 6 other fieldsHigh correlation
_BMI5 is highly correlated with _BMI5CAT and 1 other fieldsHigh correlation
_BMI5CAT is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_RFBMI5 is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_CHLDCNT is highly correlated with CHILDREN and 1 other fieldsHigh correlation
_EDUCAG is highly correlated with EDUCAHigh correlation
_INCOMG is highly correlated with INCOME2High correlation
_SMOKER3 is highly correlated with SMOKE100 and 1 other fieldsHigh correlation
_RFSMOK3 is highly correlated with _SMOKER3High correlation
DRNKANY5 is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
DROCDY3_ is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_RFBING5 is highly correlated with _RFDRHV6High correlation
_DRNKWEK is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_RFDRHV6 is highly correlated with _RFBING5High correlation
_RFSEAT2 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_RFSEAT3 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_DRNKDRV is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_AIDTST3 is highly correlated with HIVTST6High correlation
GENHLTH is highly correlated with _RFHLTHHigh correlation
PHYSHLTH is highly correlated with _PHYS14DHigh correlation
MENTHLTH is highly correlated with _MENT14DHigh correlation
EXERANY2 is highly correlated with _TOTINDAHigh correlation
CVDINFR4 is highly correlated with _MICHDHigh correlation
CVDCRHD4 is highly correlated with _MICHDHigh correlation
ASTHMA3 is highly correlated with _LTASTH1 and 2 other fieldsHigh correlation
HAVARTH3 is highly correlated with _DRDXAR1High correlation
LASTDEN4 is highly correlated with _DENVST3High correlation
RMVTETH4 is highly correlated with _EXTETH3High correlation
EDUCA is highly correlated with _EDUCAGHigh correlation
CHILDREN is highly correlated with _CHLDCNTHigh correlation
INCOME2 is highly correlated with _INCOMGHigh correlation
DECIDE is highly correlated with DIFFALONHigh correlation
DIFFWALK is highly correlated with DIFFALON and 1 other fieldsHigh correlation
DIFFALON is highly correlated with DECIDE and 1 other fieldsHigh correlation
SMOKE100 is highly correlated with _SMOKER3High correlation
USENOW3 is highly correlated with _HISPANCHigh correlation
ALCDAY5 is highly correlated with DRNKANY5 and 3 other fieldsHigh correlation
SEATBELT is highly correlated with _RFSEAT2 and 1 other fieldsHigh correlation
HIVTST6 is highly correlated with _AIDTST3High correlation
_METSTAT is highly correlated with _URBSTATHigh correlation
_URBSTAT is highly correlated with _METSTATHigh correlation
_IMPRACE is highly correlated with _PRACE1 and 5 other fieldsHigh correlation
_RFHLTH is highly correlated with GENHLTH and 1 other fieldsHigh correlation
_PHYS14D is highly correlated with PHYSHLTHHigh correlation
_MENT14D is highly correlated with MENTHLTHHigh correlation
_HCVU651 is highly correlated with _AGEG5YR and 3 other fieldsHigh correlation
_TOTINDA is highly correlated with EXERANY2High correlation
_MICHD is highly correlated with CVDINFR4 and 1 other fieldsHigh correlation
_LTASTH1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_CASTHM1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_ASTHMS1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_DRDXAR1 is highly correlated with HAVARTH3High correlation
_EXTETH3 is highly correlated with RMVTETH4High correlation
_DENVST3 is highly correlated with LASTDEN4High correlation
_PRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_MRACE1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_HISPANC is highly correlated with USENOW3High correlation
_RACE is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACEG21 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACEGR3 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_RACE_G1 is highly correlated with _IMPRACE and 5 other fieldsHigh correlation
_AGEG5YR is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_AGE65YR is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_AGE80 is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_AGE_G is highly correlated with _HCVU651 and 3 other fieldsHigh correlation
_BMI5 is highly correlated with _BMI5CAT and 1 other fieldsHigh correlation
_BMI5CAT is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_RFBMI5 is highly correlated with _BMI5 and 1 other fieldsHigh correlation
_CHLDCNT is highly correlated with CHILDRENHigh correlation
_EDUCAG is highly correlated with EDUCAHigh correlation
_INCOMG is highly correlated with INCOME2High correlation
_SMOKER3 is highly correlated with SMOKE100 and 1 other fieldsHigh correlation
_RFSMOK3 is highly correlated with _SMOKER3High correlation
DRNKANY5 is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
DROCDY3_ is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_RFBING5 is highly correlated with _RFDRHV6High correlation
_DRNKWEK is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_RFDRHV6 is highly correlated with _RFBING5High correlation
_RFSEAT2 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_RFSEAT3 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_DRNKDRV is highly correlated with ALCDAY5 and 3 other fieldsHigh correlation
_AIDTST3 is highly correlated with HIVTST6High correlation
CHECKUP1 is highly correlated with _AGE_G and 2 other fieldsHigh correlation
_DENVST3 is highly correlated with EDUCA and 3 other fieldsHigh correlation
ADDEPEV2 is highly correlated with _MENT14D and 1 other fieldsHigh correlation
PNEUVAC4 is highly correlated with _HCVU651 and 2 other fieldsHigh correlation
ALCDAY5 is highly correlated with _HCVU651 and 6 other fieldsHigh correlation
_RACEG21 is highly correlated with _RACE_G1 and 5 other fieldsHigh correlation
_PHYS14D is highly correlated with PHYSHLTH and 6 other fieldsHigh correlation
PHYSHLTH is highly correlated with _PHYS14D and 6 other fieldsHigh correlation
ASTHMA3 is highly correlated with _ASTHMS1 and 2 other fieldsHigh correlation
_RACE_G1 is highly correlated with _RACEG21 and 9 other fieldsHigh correlation
EDUCA is highly correlated with _DENVST3 and 8 other fieldsHigh correlation
CHCOCNCR is highly correlated with _PHYS14DHigh correlation
PERSDOC2 is highly correlated with _AGEG5YRHigh correlation
_CHLDCNT is highly correlated with _AGE_G and 7 other fieldsHigh correlation
_RFSEAT3 is highly correlated with SEATBELT and 1 other fieldsHigh correlation
_METSTAT is highly correlated with _URBSTATHigh correlation
_HCVU651 is highly correlated with PNEUVAC4 and 11 other fieldsHigh correlation
CVDINFR4 is highly correlated with _MICHDHigh correlation
_EDUCAG is highly correlated with EDUCA and 3 other fieldsHigh correlation
_ASTHMS1 is highly correlated with ASTHMA3 and 4 other fieldsHigh correlation
_AGE_G is highly correlated with CHECKUP1 and 8 other fieldsHigh correlation
_RFDRHV6 is highly correlated with ALCDAY5 and 8 other fieldsHigh correlation
LASTDEN4 is highly correlated with _DENVST3 and 2 other fieldsHigh correlation
FLUSHOT6 is highly correlated with _CHLDCNT and 3 other fieldsHigh correlation
_LTASTH1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_INCOMG is highly correlated with _HCVU651 and 2 other fieldsHigh correlation
DECIDE is highly correlated with _ASTHMS1 and 3 other fieldsHigh correlation
SEATBELT is highly correlated with _RFSEAT3 and 2 other fieldsHigh correlation
_BMI5CAT is highly correlated with _RFBMI5 and 1 other fieldsHigh correlation
RMVTETH4 is highly correlated with _DENVST3 and 8 other fieldsHigh correlation
_RFSMOK3 is highly correlated with _SMOKER3High correlation
DIFFDRES is highly correlated with LASTDEN4 and 1 other fieldsHigh correlation
_DRNKWEK is highly correlated with _RFDRHV6 and 4 other fieldsHigh correlation
MEDCOST is highly correlated with _BMI5High correlation
_AGEG5YR is highly correlated with CHECKUP1 and 11 other fieldsHigh correlation
_CASTHM1 is highly correlated with ASTHMA3 and 2 other fieldsHigh correlation
_URBSTAT is highly correlated with _METSTATHigh correlation
_RFBMI5 is highly correlated with _BMI5CAT and 1 other fieldsHigh correlation
_RACE is highly correlated with _RACEG21 and 9 other fieldsHigh correlation
DIABETE3 is highly correlated with _IMPRACE and 1 other fieldsHigh correlation
HAVARTH3 is highly correlated with _PHYS14D and 2 other fieldsHigh correlation
CHILDREN is highly correlated with _CHLDCNT and 7 other fieldsHigh correlation
_RFSEAT2 is highly correlated with _RFSEAT3 and 1 other fieldsHigh correlation
SLEPTIM1 is highly correlated with PHYSHLTH and 2 other fieldsHigh correlation
_RACEGR3 is highly correlated with _RACEG21 and 9 other fieldsHigh correlation
_TOTINDA is highly correlated with EXERANY2High correlation
DROCDY3_ is highly correlated with ALCDAY5 and 4 other fieldsHigh correlation
_DUALUSE is highly correlated with EDUCA and 1 other fieldsHigh correlation
_MRACE1 is highly correlated with _RACEG21 and 7 other fieldsHigh correlation
_AGE80 is highly correlated with CHECKUP1 and 10 other fieldsHigh correlation
_AGE65YR is highly correlated with _CHLDCNT and 7 other fieldsHigh correlation
_DRNKDRV is highly correlated with ALCDAY5 and 2 other fieldsHigh correlation
USENOW3 is highly correlated with ALCDAY5 and 10 other fieldsHigh correlation
MARITAL is highly correlated with _AGE_G and 2 other fieldsHigh correlation
_EXTETH3 is highly correlated with EDUCA and 2 other fieldsHigh correlation
SMOKE100 is highly correlated with _SMOKER3High correlation
CHCCOPD1 is highly correlated with PHYSHLTH and 4 other fieldsHigh correlation
_SMOKER3 is highly correlated with _RFSMOK3 and 1 other fieldsHigh correlation
_IMPRACE is highly correlated with _RACEG21 and 11 other fieldsHigh correlation
_DRDXAR1 is highly correlated with _PHYS14D and 2 other fieldsHigh correlation
HIVRISK5 is highly correlated with _HCVU651 and 4 other fieldsHigh correlation
DRNKANY5 is highly correlated with ALCDAY5 and 2 other fieldsHigh correlation
_MENT14D is highly correlated with ADDEPEV2 and 3 other fieldsHigh correlation
INCOME2 is highly correlated with _INCOMG and 1 other fieldsHigh correlation
EXERANY2 is highly correlated with _TOTINDAHigh correlation
_HISPANC is highly correlated with _RACE_G1 and 5 other fieldsHigh correlation
HLTHPLN1 is highly correlated with _CHLDCNT and 5 other fieldsHigh correlation
DIFFWALK is highly correlated with _PHYS14D and 3 other fieldsHigh correlation
DIFFALON is highly correlated with _PHYS14D and 5 other fieldsHigh correlation
_BMI5 is highly correlated with SEATBELT and 3 other fieldsHigh correlation
MENTHLTH is highly correlated with ADDEPEV2 and 11 other fieldsHigh correlation
_RFBING5 is highly correlated with _RACE_G1 and 7 other fieldsHigh correlation
RENTHOM1 is highly correlated with USENOW3High correlation
_MICHD is highly correlated with CVDINFR4 and 1 other fieldsHigh correlation
VETERAN3 is highly correlated with USENOW3 and 1 other fieldsHigh correlation
_RFHLTH is highly correlated with _PHYS14D and 4 other fieldsHigh correlation
CVDCRHD4 is highly correlated with _MICHDHigh correlation
BLIND is highly correlated with EDUCA and 2 other fieldsHigh correlation
_PRACE1 is highly correlated with _RACEG21 and 7 other fieldsHigh correlation
HIVTST6 is highly correlated with HIVRISK5 and 1 other fieldsHigh correlation
EMPLOY1 is highly correlated with _DENVST3 and 10 other fieldsHigh correlation
GENHLTH is highly correlated with _RFHLTHHigh correlation
_AIDTST3 is highly correlated with HIVRISK5 and 1 other fieldsHigh correlation
SEX1 is highly correlated with EDUCA and 6 other fieldsHigh correlation
QSTVER is highly correlated with CHECKUP1 and 74 other fieldsHigh correlation
CHECKUP1 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_RACEGR3 is highly correlated with QSTVER and 12 other fieldsHigh correlation
_DENVST3 is highly correlated with QSTVER and 2 other fieldsHigh correlation
ADDEPEV2 is highly correlated with QSTVER and 2 other fieldsHigh correlation
PNEUVAC4 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_TOTINDA is highly correlated with QSTVER and 3 other fieldsHigh correlation
_RACEG21 is highly correlated with QSTVER and 8 other fieldsHigh correlation
_DUALUSE is highly correlated with QSTVER and 2 other fieldsHigh correlation
_PHYS14D is highly correlated with QSTVER and 2 other fieldsHigh correlation
_MRACE1 is highly correlated with QSTVER and 8 other fieldsHigh correlation
_AGE65YR is highly correlated with QSTVER and 4 other fieldsHigh correlation
_DRNKDRV is highly correlated with QSTVER and 3 other fieldsHigh correlation
ASTHMA3 is highly correlated with QSTVER and 5 other fieldsHigh correlation
_RACE_G1 is highly correlated with QSTVER and 12 other fieldsHigh correlation
SMOKE100 is highly correlated with QSTVER and 3 other fieldsHigh correlation
CHCOCNCR is highly correlated with QSTVER and 2 other fieldsHigh correlation
CHCCOPD1 is highly correlated with QSTVER and 2 other fieldsHigh correlation
MARITAL is highly correlated with QSTVER and 2 other fieldsHigh correlation
USENOW3 is highly correlated with QSTVER and 7 other fieldsHigh correlation
_EXTETH3 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_IMPRACE is highly correlated with QSTVER and 12 other fieldsHigh correlation
PERSDOC2 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_SMOKER3 is highly correlated with QSTVER and 4 other fieldsHigh correlation
_DRDXAR1 is highly correlated with QSTVER and 3 other fieldsHigh correlation
HIVRISK5 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_CHLDCNT is highly correlated with QSTVER and 3 other fieldsHigh correlation
_MENT14D is highly correlated with QSTVER and 2 other fieldsHigh correlation
DRNKANY5 is highly correlated with QSTVER and 3 other fieldsHigh correlation
_RFSEAT3 is highly correlated with QSTVER and 4 other fieldsHigh correlation
_METSTAT is highly correlated with QSTVER and 2 other fieldsHigh correlation
_HCVU651 is highly correlated with QSTVER and 5 other fieldsHigh correlation
EXERANY2 is highly correlated with QSTVER and 3 other fieldsHigh correlation
CVDINFR4 is highly correlated with QSTVER and 3 other fieldsHigh correlation
_ASTHMS1 is highly correlated with QSTVER and 5 other fieldsHigh correlation
CHCKDNY1 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_AGE_G is highly correlated with QSTVER and 5 other fieldsHigh correlation
_HISPANC is highly correlated with QSTVER and 9 other fieldsHigh correlation
HLTHPLN1 is highly correlated with QSTVER and 4 other fieldsHigh correlation
_EDUCAG is highly correlated with QSTVER and 3 other fieldsHigh correlation
DIFFWALK is highly correlated with QSTVER and 4 other fieldsHigh correlation
DIFFALON is highly correlated with QSTVER and 4 other fieldsHigh correlation
_RFDRHV6 is highly correlated with QSTVER and 8 other fieldsHigh correlation
FLUSHOT6 is highly correlated with QSTVER and 2 other fieldsHigh correlation
QSTLANG is highly correlated with QSTVER and 74 other fieldsHigh correlation
_LTASTH1 is highly correlated with QSTVER and 5 other fieldsHigh correlation
DECIDE is highly correlated with QSTVER and 3 other fieldsHigh correlation
_RFBING5 is highly correlated with QSTVER and 8 other fieldsHigh correlation
CVDSTRK3 is highly correlated with QSTVER and 2 other fieldsHigh correlation
SEATBELT is highly correlated with QSTVER and 4 other fieldsHigh correlation
CHCSCNCR is highly correlated with QSTVER and 2 other fieldsHigh correlation
_BMI5CAT is highly correlated with QSTVER and 3 other fieldsHigh correlation
RENTHOM1 is highly correlated with QSTVER and 2 other fieldsHigh correlation
_RFSMOK3 is highly correlated with QSTVER and 3 other fieldsHigh correlation
_MICHD is highly correlated with QSTVER and 4 other fieldsHigh correlation
VETERAN3 is highly correlated with QSTVER and 2 other fieldsHigh correlation
CVDCRHD4 is highly correlated with QSTVER and 3 other fieldsHigh correlation
DIFFDRES is highly correlated with QSTVER and 2 other fieldsHigh correlation
_RFHLTH is highly correlated with QSTVER and 3 other fieldsHigh correlation
_AIDTST3 is highly correlated with QSTVER and 3 other fieldsHigh correlation
DEAF is highly correlated with QSTVER and 2 other fieldsHigh correlation
_STATE is highly correlated with QSTVER and 74 other fieldsHigh correlation
BLIND is highly correlated with QSTVER and 2 other fieldsHigh correlation
HIVTST6 is highly correlated with QSTVER and 3 other fieldsHigh correlation
_PRACE1 is highly correlated with QSTVER and 9 other fieldsHigh correlation
MEDCOST is highly correlated with QSTVER and 2 other fieldsHigh correlation
GENHLTH is highly correlated with QSTVER and 4 other fieldsHigh correlation
_CASTHM1 is highly correlated with QSTVER and 5 other fieldsHigh correlation
_URBSTAT is highly correlated with QSTVER and 2 other fieldsHigh correlation
_RFBMI5 is highly correlated with QSTVER and 3 other fieldsHigh correlation
_RACE is highly correlated with QSTVER and 12 other fieldsHigh correlation
DIABETE3 is highly correlated with QSTVER and 3 other fieldsHigh correlation
HAVARTH3 is highly correlated with QSTVER and 3 other fieldsHigh correlation
CHILDREN is highly correlated with QSTVER and 3 other fieldsHigh correlation
_RFSEAT2 is highly correlated with QSTVER and 4 other fieldsHigh correlation
SEX1 is highly correlated with QSTVER and 3 other fieldsHigh correlation
FLUSHOT6 is uniformly distributed Uniform

Reproduction

Analysis started2021-06-01 14:11:43.531171
Analysis finished2021-06-01 14:12:48.737935
Duration1 minute and 5.21 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

_STATE
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
100 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0100
100.0%

Length

2021-06-01T19:12:48.943880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:49.020855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0100
100.0%

Most occurring characters

ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1100
50.0%
0100
50.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

GENHLTH
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
35 
2.0
27 
4.0
14 
5.0
12 
1.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row5.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.035
35.0%
2.027
27.0%
4.014
 
14.0%
5.012
 
12.0%
1.012
 
12.0%

Length

2021-06-01T19:12:49.204085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:49.292463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.035
35.0%
2.027
27.0%
4.014
 
14.0%
1.012
 
12.0%
5.012
 
12.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
335
 
11.7%
227
 
9.0%
414
 
4.7%
512
 
4.0%
112
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
335
 
17.5%
227
 
13.5%
414
 
7.0%
512
 
6.0%
112
 
6.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
335
 
11.7%
227
 
9.0%
414
 
4.7%
512
 
4.0%
112
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
335
 
11.7%
227
 
9.0%
414
 
4.7%
512
 
4.0%
112
 
4.0%

PHYSHLTH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.01
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:49.513451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q118.75
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)69.25

Descriptive statistics

Standard deviation36.97760584
Coefficient of variation (CV)0.6374350256
Kurtosis-1.643887949
Mean58.01
Median Absolute Deviation (MAD)0
Skewness-0.4891340719
Sum5801
Variance1367.343333
MonotonicityNot monotonic
2021-06-01T19:12:49.617719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8857
57.0%
3014
 
14.0%
26
 
6.0%
55
 
5.0%
34
 
4.0%
103
 
3.0%
202
 
2.0%
142
 
2.0%
72
 
2.0%
991
 
1.0%
Other values (4)4
 
4.0%
ValueCountFrequency (%)
11
 
1.0%
26
6.0%
34
4.0%
55
5.0%
72
 
2.0%
103
3.0%
121
 
1.0%
142
 
2.0%
151
 
1.0%
202
 
2.0%
ValueCountFrequency (%)
991
 
1.0%
8857
57.0%
771
 
1.0%
3014
 
14.0%
202
 
2.0%
151
 
1.0%
142
 
2.0%
121
 
1.0%
103
 
3.0%
72
 
2.0%

MENTHLTH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.41
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:49.730133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q177
median88
Q388
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)11

Descriptive statistics

Standard deviation33.48453435
Coefficient of variation (CV)0.4824165732
Kurtosis-0.1663766513
Mean69.41
Median Absolute Deviation (MAD)0
Skewness-1.309242692
Sum6941
Variance1121.21404
MonotonicityNot monotonic
2021-06-01T19:12:49.837831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8873
73.0%
26
 
6.0%
305
 
5.0%
104
 
4.0%
33
 
3.0%
13
 
3.0%
772
 
2.0%
152
 
2.0%
201
 
1.0%
991
 
1.0%
ValueCountFrequency (%)
13
 
3.0%
26
 
6.0%
33
 
3.0%
104
 
4.0%
152
 
2.0%
201
 
1.0%
305
 
5.0%
772
 
2.0%
8873
73.0%
991
 
1.0%
ValueCountFrequency (%)
991
 
1.0%
8873
73.0%
772
 
2.0%
305
 
5.0%
201
 
1.0%
152
 
2.0%
104
 
4.0%
33
 
3.0%
26
 
6.0%
13
 
3.0%

HLTHPLN1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
92 
2.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.092
92.0%
2.08
 
8.0%

Length

2021-06-01T19:12:50.041427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:50.112273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.092
92.0%
2.08
 
8.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
192
30.7%
28
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
192
46.0%
28
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
192
30.7%
28
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
192
30.7%
28
 
2.7%

PERSDOC2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
77 
2.0
17 
3.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.077
77.0%
2.017
 
17.0%
3.06
 
6.0%

Length

2021-06-01T19:12:50.312973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:50.390789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.077
77.0%
2.017
 
17.0%
3.06
 
6.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
177
25.7%
217
 
5.7%
36
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
177
38.5%
217
 
8.5%
36
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
177
25.7%
217
 
5.7%
36
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
177
25.7%
217
 
5.7%
36
 
2.0%

MEDCOST
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
94 
1.0
 
5
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.094
94.0%
1.05
 
5.0%
9.01
 
1.0%

Length

2021-06-01T19:12:50.600194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:50.681976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.094
94.0%
1.05
 
5.0%
9.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
294
31.3%
15
 
1.7%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
294
47.0%
15
 
2.5%
91
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
294
31.3%
15
 
1.7%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
294
31.3%
15
 
1.7%
91
 
0.3%

CHECKUP1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
88 
2.0
 
5
7.0
 
3
4.0
 
3
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.088
88.0%
2.05
 
5.0%
7.03
 
3.0%
4.03
 
3.0%
3.01
 
1.0%

Length

2021-06-01T19:12:50.908771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:50.991550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.088
88.0%
2.05
 
5.0%
4.03
 
3.0%
7.03
 
3.0%
3.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
188
29.3%
25
 
1.7%
43
 
1.0%
73
 
1.0%
31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
188
44.0%
25
 
2.5%
43
 
1.5%
73
 
1.5%
31
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
188
29.3%
25
 
1.7%
43
 
1.0%
73
 
1.0%
31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
188
29.3%
25
 
1.7%
43
 
1.0%
73
 
1.0%
31
 
0.3%

EXERANY2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
54 
2.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Length

2021-06-01T19:12:51.209916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:51.281726image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
154
27.0%
246
23.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

SLEPTIM1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.54
Minimum4
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:51.353143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.95
Q16
median7
Q38
95-th percentile10.1
Maximum77
Range73
Interquartile range (IQR)2

Descriptive statistics

Standard deviation9.971089522
Coefficient of variation (CV)1.167574886
Kurtosis44.60969897
Mean8.54
Median Absolute Deviation (MAD)1
Skewness6.660058699
Sum854
Variance99.42262626
MonotonicityNot monotonic
2021-06-01T19:12:51.450769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
626
26.0%
823
23.0%
722
22.0%
107
 
7.0%
57
 
7.0%
95
 
5.0%
45
 
5.0%
123
 
3.0%
772
 
2.0%
ValueCountFrequency (%)
45
 
5.0%
57
 
7.0%
626
26.0%
722
22.0%
823
23.0%
95
 
5.0%
107
 
7.0%
123
 
3.0%
772
 
2.0%
ValueCountFrequency (%)
772
 
2.0%
123
 
3.0%
107
 
7.0%
95
 
5.0%
823
23.0%
722
22.0%
626
26.0%
57
 
7.0%
45
 
5.0%

CVDINFR4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
92 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.092
92.0%
1.08
 
8.0%

Length

2021-06-01T19:12:51.659038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:51.728853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.092
92.0%
1.08
 
8.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
292
46.0%
18
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

CVDCRHD4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
91 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.091
91.0%
1.09
 
9.0%

Length

2021-06-01T19:12:51.897511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:51.966686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.091
91.0%
1.09
 
9.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
291
45.5%
19
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

CVDSTRK3
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
93 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.093
93.0%
1.07
 
7.0%

Length

2021-06-01T19:12:52.135238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:52.203054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.093
93.0%
1.07
 
7.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
293
31.0%
17
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
293
46.5%
17
 
3.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
293
31.0%
17
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
293
31.0%
17
 
2.3%

ASTHMA3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
87 
1.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.087
87.0%
1.013
 
13.0%

Length

2021-06-01T19:12:52.389577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:52.461408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.087
87.0%
1.013
 
13.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
287
43.5%
113
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

CHCSCNCR
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
86 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.086
86.0%
1.014
 
14.0%

Length

2021-06-01T19:12:52.644119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:52.714932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.086
86.0%
1.014
 
14.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
286
43.0%
114
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

CHCOCNCR
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
84 
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.084
84.0%
1.016
 
16.0%

Length

2021-06-01T19:12:53.025113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:53.091227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.084
84.0%
1.016
 
16.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
284
28.0%
116
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
284
42.0%
116
 
8.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
284
28.0%
116
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
284
28.0%
116
 
5.3%

CHCCOPD1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
92 
1.0
 
7
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.092
92.0%
1.07
 
7.0%
7.01
 
1.0%

Length

2021-06-01T19:12:53.269834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:53.339158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.092
92.0%
1.07
 
7.0%
7.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
17
 
2.3%
71
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
292
46.0%
17
 
3.5%
71
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
17
 
2.3%
71
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
17
 
2.3%
71
 
0.3%

HAVARTH3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
54 
2.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Length

2021-06-01T19:12:53.533867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:53.601718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
154
27.0%
246
23.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

ADDEPEV2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
75 
1.0
25 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.075
75.0%
1.025
 
25.0%

Length

2021-06-01T19:12:53.779486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:53.848748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.075
75.0%
1.025
 
25.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
275
25.0%
125
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
275
37.5%
125
 
12.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
275
25.0%
125
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
275
25.0%
125
 
8.3%

CHCKDNY1
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
91 
1.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.091
91.0%
1.09
 
9.0%

Length

2021-06-01T19:12:54.016660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:54.086476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.091
91.0%
1.09
 
9.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
291
45.5%
19
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
291
30.3%
19
 
3.0%

DIABETE3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
75 
1.0
22 
4.0
 
2
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row3.0
2nd row3.0
3rd row1.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.075
75.0%
1.022
 
22.0%
4.02
 
2.0%
2.01
 
1.0%

Length

2021-06-01T19:12:54.276403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:54.346218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.075
75.0%
1.022
 
22.0%
4.02
 
2.0%
2.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
375
25.0%
122
 
7.3%
42
 
0.7%
21
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
375
37.5%
122
 
11.0%
42
 
1.0%
21
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
375
25.0%
122
 
7.3%
42
 
0.7%
21
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
375
25.0%
122
 
7.3%
42
 
0.7%
21
 
0.3%

LASTDEN4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.99
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:54.421736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile4
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.604885219
Coefficient of variation (CV)0.8064749843
Kurtosis5.547248546
Mean1.99
Median Absolute Deviation (MAD)0
Skewness2.126069674
Sum199
Variance2.575656566
MonotonicityNot monotonic
2021-06-01T19:12:54.519610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
162
62.0%
415
 
15.0%
312
 
12.0%
28
 
8.0%
82
 
2.0%
91
 
1.0%
ValueCountFrequency (%)
162
62.0%
28
 
8.0%
312
 
12.0%
415
 
15.0%
82
 
2.0%
91
 
1.0%
ValueCountFrequency (%)
91
 
1.0%
82
 
2.0%
415
 
15.0%
312
 
12.0%
28
 
8.0%
162
62.0%

RMVTETH4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.33
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:54.620048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.133575104
Coefficient of variation (CV)0.7236894003
Kurtosis-1.844359724
Mean4.33
Median Absolute Deviation (MAD)2
Skewness0.2040084266
Sum433
Variance9.819292929
MonotonicityNot monotonic
2021-06-01T19:12:54.711903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
834
34.0%
130
30.0%
215
15.0%
312
 
12.0%
78
 
8.0%
91
 
1.0%
ValueCountFrequency (%)
130
30.0%
215
15.0%
312
 
12.0%
78
 
8.0%
834
34.0%
91
 
1.0%
ValueCountFrequency (%)
91
 
1.0%
834
34.0%
78
 
8.0%
312
 
12.0%
215
15.0%
130
30.0%

SEX1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
64 
1.0
35 
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.064
64.0%
1.035
35.0%
9.01
 
1.0%

Length

2021-06-01T19:12:54.924161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:54.994581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.064
64.0%
1.035
35.0%
9.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
264
21.3%
135
 
11.7%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
264
32.0%
135
 
17.5%
91
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
264
21.3%
135
 
11.7%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
264
21.3%
135
 
11.7%
91
 
0.3%

MARITAL
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
44 
3.0
33 
2.0
13 
5.0
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row5.0
3rd row3.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.044
44.0%
3.033
33.0%
2.013
 
13.0%
5.08
 
8.0%
4.02
 
2.0%

Length

2021-06-01T19:12:55.178586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:55.248435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.044
44.0%
3.033
33.0%
2.013
 
13.0%
5.08
 
8.0%
4.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
144
14.7%
333
 
11.0%
213
 
4.3%
58
 
2.7%
42
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
144
22.0%
333
 
16.5%
213
 
6.5%
58
 
4.0%
42
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
144
14.7%
333
 
11.0%
213
 
4.3%
58
 
2.7%
42
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
144
14.7%
333
 
11.0%
213
 
4.3%
58
 
2.7%
42
 
0.7%

EDUCA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7
Minimum2
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:55.325513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q35
95-th percentile6
Maximum9
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.105541597
Coefficient of variation (CV)0.2352216163
Kurtosis1.552091818
Mean4.7
Median Absolute Deviation (MAD)1
Skewness0.1189975622
Sum470
Variance1.222222222
MonotonicityNot monotonic
2021-06-01T19:12:55.416471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
536
36.0%
429
29.0%
622
22.0%
39
 
9.0%
23
 
3.0%
91
 
1.0%
ValueCountFrequency (%)
23
 
3.0%
39
 
9.0%
429
29.0%
536
36.0%
622
22.0%
91
 
1.0%
ValueCountFrequency (%)
91
 
1.0%
622
22.0%
536
36.0%
429
29.0%
39
 
9.0%
23
 
3.0%

RENTHOM1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
80 
2.0
14 
3.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.080
80.0%
2.014
 
14.0%
3.06
 
6.0%

Length

2021-06-01T19:12:55.643945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:55.715351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.080
80.0%
2.014
 
14.0%
3.06
 
6.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
180
26.7%
214
 
4.7%
36
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
180
40.0%
214
 
7.0%
36
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
180
26.7%
214
 
4.7%
36
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
180
26.7%
214
 
4.7%
36
 
2.0%

VETERAN3
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
87 
1.0
12 
7.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.087
87.0%
1.012
 
12.0%
7.01
 
1.0%

Length

2021-06-01T19:12:55.897106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:55.966159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.087
87.0%
1.012
 
12.0%
7.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
112
 
4.0%
71
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
287
43.5%
112
 
6.0%
71
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
112
 
4.0%
71
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
112
 
4.0%
71
 
0.3%

EMPLOY1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.08
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:56.174335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.762227179
Coefficient of variation (CV)0.5437455077
Kurtosis-1.470012895
Mean5.08
Median Absolute Deviation (MAD)1
Skewness-0.6070863349
Sum508
Variance7.62989899
MonotonicityNot monotonic
2021-06-01T19:12:56.259081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
749
49.0%
124
24.0%
811
 
11.0%
27
 
7.0%
54
 
4.0%
33
 
3.0%
61
 
1.0%
41
 
1.0%
ValueCountFrequency (%)
124
24.0%
27
 
7.0%
33
 
3.0%
41
 
1.0%
54
 
4.0%
61
 
1.0%
749
49.0%
811
 
11.0%
ValueCountFrequency (%)
811
 
11.0%
749
49.0%
61
 
1.0%
54
 
4.0%
41
 
1.0%
33
 
3.0%
27
 
7.0%
124
24.0%

CHILDREN
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
88.0
84 
1.0
10 
2.0
 
5
4.0
 
1

Length

Max length4
Median length4
Mean length3.84
Min length3

Characters and Unicode

Total characters384
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row88.0
2nd row2.0
3rd row88.0
4th row88.0
5th row88.0

Common Values

ValueCountFrequency (%)
88.084
84.0%
1.010
 
10.0%
2.05
 
5.0%
4.01
 
1.0%

Length

2021-06-01T19:12:56.455227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:56.532841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
88.084
84.0%
1.010
 
10.0%
2.05
 
5.0%
4.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
8168
43.8%
.100
26.0%
0100
26.0%
110
 
2.6%
25
 
1.3%
41
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number284
74.0%
Other Punctuation100
 
26.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8168
59.2%
0100
35.2%
110
 
3.5%
25
 
1.8%
41
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8168
43.8%
.100
26.0%
0100
26.0%
110
 
2.6%
25
 
1.3%
41
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8168
43.8%
.100
26.0%
0100
26.0%
110
 
2.6%
25
 
1.3%
41
 
0.3%

INCOME2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.97
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:56.611342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median7
Q38
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation35.14025434
Coefficient of variation (CV)1.466009776
Kurtosis0.207201612
Mean23.97
Median Absolute Deviation (MAD)2
Skewness1.439440584
Sum2397
Variance1234.837475
MonotonicityNot monotonic
2021-06-01T19:12:56.709080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
821
21.0%
616
16.0%
9912
12.0%
7710
10.0%
510
10.0%
79
9.0%
28
 
8.0%
37
 
7.0%
46
 
6.0%
11
 
1.0%
ValueCountFrequency (%)
11
 
1.0%
28
 
8.0%
37
 
7.0%
46
 
6.0%
510
10.0%
616
16.0%
79
9.0%
821
21.0%
7710
10.0%
9912
12.0%
ValueCountFrequency (%)
9912
12.0%
7710
10.0%
821
21.0%
79
9.0%
616
16.0%
510
10.0%
46
 
6.0%
37
 
7.0%
28
 
8.0%
11
 
1.0%

DEAF
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
92 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.092
92.0%
1.08
 
8.0%

Length

2021-06-01T19:12:56.900996image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:56.976331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.092
92.0%
1.08
 
8.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
292
46.0%
18
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
292
30.7%
18
 
2.7%

BLIND
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
95 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.095
95.0%
1.05
 
5.0%

Length

2021-06-01T19:12:57.146720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:57.216133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.095
95.0%
1.05
 
5.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
295
31.7%
15
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
295
47.5%
15
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
295
31.7%
15
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
295
31.7%
15
 
1.7%

DECIDE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
85 
1.0
13 
7.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.085
85.0%
1.013
 
13.0%
7.02
 
2.0%

Length

2021-06-01T19:12:57.394961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:57.476781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.085
85.0%
1.013
 
13.0%
7.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
285
28.3%
113
 
4.3%
72
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
285
42.5%
113
 
6.5%
72
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
285
28.3%
113
 
4.3%
72
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
285
28.3%
113
 
4.3%
72
 
0.7%

DIFFWALK
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
74 
1.0
26 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.074
74.0%
1.026
 
26.0%

Length

2021-06-01T19:12:57.658846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:57.730568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.074
74.0%
1.026
 
26.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
274
24.7%
126
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
274
37.0%
126
 
13.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
274
24.7%
126
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
274
24.7%
126
 
8.7%

DIFFDRES
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
96 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.096
96.0%
1.04
 
4.0%

Length

2021-06-01T19:12:57.908170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:57.978591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.096
96.0%
1.04
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
296
32.0%
14
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
296
48.0%
14
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
296
32.0%
14
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
296
32.0%
14
 
1.3%

DIFFALON
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
87 
1.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.087
87.0%
1.013
 
13.0%

Length

2021-06-01T19:12:58.164629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:58.234538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.087
87.0%
1.013
 
13.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
287
43.5%
113
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
287
29.0%
113
 
4.3%

SMOKE100
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
58 
1.0
42 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.058
58.0%
1.042
42.0%

Length

2021-06-01T19:12:58.418522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:58.485380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.058
58.0%
1.042
42.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
258
19.3%
142
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
258
29.0%
142
21.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
258
19.3%
142
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
258
19.3%
142
14.0%

USENOW3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
98 
1.0
 
1
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.098
98.0%
1.01
 
1.0%
2.01
 
1.0%

Length

2021-06-01T19:12:58.671205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:58.741053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.098
98.0%
2.01
 
1.0%
1.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
398
32.7%
11
 
0.3%
21
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
398
49.0%
11
 
0.5%
21
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
398
32.7%
11
 
0.3%
21
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
398
32.7%
11
 
0.3%
21
 
0.3%

ALCDAY5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean650.18
Minimum101
Maximum888
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:12:58.813445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile106.8
Q1204.75
median888
Q3888
95-th percentile888
Maximum888
Range787
Interquartile range (IQR)683.25

Descriptive statistics

Standard deviation333.870034
Coefficient of variation (CV)0.513504005
Kurtosis-1.508668831
Mean650.18
Median Absolute Deviation (MAD)0
Skewness-0.703217864
Sum65018
Variance111469.1996
MonotonicityNot monotonic
2021-06-01T19:12:58.912183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
88866
66.0%
20111
 
11.0%
2026
 
6.0%
2103
 
3.0%
2303
 
3.0%
1013
 
3.0%
2202
 
2.0%
1021
 
1.0%
1031
 
1.0%
2051
 
1.0%
Other values (3)3
 
3.0%
ValueCountFrequency (%)
1013
 
3.0%
1021
 
1.0%
1031
 
1.0%
1071
 
1.0%
20111
11.0%
2026
6.0%
2031
 
1.0%
2041
 
1.0%
2051
 
1.0%
2103
 
3.0%
ValueCountFrequency (%)
88866
66.0%
2303
 
3.0%
2202
 
2.0%
2103
 
3.0%
2051
 
1.0%
2041
 
1.0%
2031
 
1.0%
2026
 
6.0%
20111
 
11.0%
1071
 
1.0%

FLUSHOT6
Categorical

HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
50 
2.0
50 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.050
50.0%
2.050
50.0%

Length

2021-06-01T19:12:59.128248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:59.197100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.050
50.0%
1.050
50.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
150
16.7%
250
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
150
25.0%
250
25.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
150
16.7%
250
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
150
16.7%
250
16.7%

PNEUVAC4
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
53 
2.0
43 
7.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.053
53.0%
2.043
43.0%
7.04
 
4.0%

Length

2021-06-01T19:12:59.505702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:59.574519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.053
53.0%
2.043
43.0%
7.04
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
153
17.7%
243
14.3%
74
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
153
26.5%
243
21.5%
74
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
153
17.7%
243
14.3%
74
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
153
17.7%
243
14.3%
74
 
1.3%

SEATBELT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
94 
3.0
 
3
5.0
 
2
2.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.094
94.0%
3.03
 
3.0%
5.02
 
2.0%
2.01
 
1.0%

Length

2021-06-01T19:12:59.746643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:12:59.817492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.094
94.0%
3.03
 
3.0%
5.02
 
2.0%
2.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
33
 
1.0%
52
 
0.7%
21
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
194
47.0%
33
 
1.5%
52
 
1.0%
21
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
33
 
1.0%
52
 
0.7%
21
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
33
 
1.0%
52
 
0.7%
21
 
0.3%

HIVTST6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
81 
1.0
17 
7.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.081
81.0%
1.017
 
17.0%
7.02
 
2.0%

Length

2021-06-01T19:13:00.041856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:00.112666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.081
81.0%
1.017
 
17.0%
7.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
72
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
281
40.5%
117
 
8.5%
72
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
72
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
72
 
0.7%

HIVRISK5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
98 
9.0
 
1
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.098
98.0%
9.01
 
1.0%
1.01
 
1.0%

Length

2021-06-01T19:13:00.299959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:00.368694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.098
98.0%
1.01
 
1.0%
9.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
298
32.7%
11
 
0.3%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
298
49.0%
11
 
0.5%
91
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
298
32.7%
11
 
0.3%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
298
32.7%
11
 
0.3%
91
 
0.3%

QSTVER
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
10.0
100 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0100
100.0%

Length

2021-06-01T19:13:00.539357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:00.610569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
10.0100
100.0%

Most occurring characters

ValueCountFrequency (%)
0200
50.0%
1100
25.0%
.100
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number300
75.0%
Other Punctuation100
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0200
66.7%
1100
33.3%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0200
50.0%
1100
25.0%
.100
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0200
50.0%
1100
25.0%
.100
25.0%

QSTLANG
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
100 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0100
100.0%

Length

2021-06-01T19:13:00.774587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:00.841437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0100
100.0%

Most occurring characters

ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1100
50.0%
0100
50.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1100
33.3%
.100
33.3%
0100
33.3%

_METSTAT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
70 
2.0
30 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.070
70.0%
2.030
30.0%

Length

2021-06-01T19:13:01.017570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:01.085427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.070
70.0%
2.030
30.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
170
23.3%
230
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
170
35.0%
230
 
15.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
170
23.3%
230
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
170
23.3%
230
 
10.0%

_URBSTAT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
89 
2.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.089
89.0%
2.011
 
11.0%

Length

2021-06-01T19:13:01.269441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:01.337706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.089
89.0%
2.011
 
11.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
189
29.7%
211
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
189
44.5%
211
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
189
29.7%
211
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
189
29.7%
211
 
3.7%

_IMPRACE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
81 
2.0
13 
6.0
 
5
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
6.05
 
5.0%
5.01
 
1.0%

Length

2021-06-01T19:13:01.521972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:01.594778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
6.05
 
5.0%
5.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
65
 
1.7%
51
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
181
40.5%
213
 
6.5%
65
 
2.5%
51
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
65
 
1.7%
51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
65
 
1.7%
51
 
0.3%

_DUALUSE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
86 
9.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row9.0
4th row9.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.086
86.0%
9.014
 
14.0%

Length

2021-06-01T19:13:01.781285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:01.847824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.086
86.0%
9.014
 
14.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
186
28.7%
914
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
186
43.0%
914
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
186
28.7%
914
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
186
28.7%
914
 
4.7%

_RFHLTH
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
74 
2.0
26 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.074
74.0%
2.026
 
26.0%

Length

2021-06-01T19:13:02.014993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:02.081195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.074
74.0%
2.026
 
26.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
174
24.7%
226
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
174
37.0%
226
 
13.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
174
24.7%
226
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
174
24.7%
226
 
8.7%

_PHYS14D
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
57 
2.0
22 
3.0
19 
9.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.057
57.0%
2.022
 
22.0%
3.019
 
19.0%
9.02
 
2.0%

Length

2021-06-01T19:13:02.260261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:02.329981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.057
57.0%
2.022
 
22.0%
3.019
 
19.0%
9.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
157
19.0%
222
 
7.3%
319
 
6.3%
92
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
157
28.5%
222
 
11.0%
319
 
9.5%
92
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
157
19.0%
222
 
7.3%
319
 
6.3%
92
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
157
19.0%
222
 
7.3%
319
 
6.3%
92
 
0.7%

_MENT14D
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
73 
2.0
16 
3.0
9.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.073
73.0%
2.016
 
16.0%
3.08
 
8.0%
9.03
 
3.0%

Length

2021-06-01T19:13:02.666016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:02.732837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.073
73.0%
2.016
 
16.0%
3.08
 
8.0%
9.03
 
3.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
173
24.3%
216
 
5.3%
38
 
2.7%
93
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
173
36.5%
216
 
8.0%
38
 
4.0%
93
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
173
24.3%
216
 
5.3%
38
 
2.7%
93
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
173
24.3%
216
 
5.3%
38
 
2.7%
93
 
1.0%

_HCVU651
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
9.0
60 
1.0
32 
2.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row2.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.060
60.0%
1.032
32.0%
2.08
 
8.0%

Length

2021-06-01T19:13:02.913057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:02.981910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
9.060
60.0%
1.032
32.0%
2.08
 
8.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
960
20.0%
132
 
10.7%
28
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
960
30.0%
132
 
16.0%
28
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
960
20.0%
132
 
10.7%
28
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
960
20.0%
132
 
10.7%
28
 
2.7%

_TOTINDA
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
54 
2.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Length

2021-06-01T19:13:03.167230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:03.233057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
154
27.0%
246
23.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

_MICHD
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
86 
1.0
14 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.086
86.0%
1.014
 
14.0%

Length

2021-06-01T19:13:03.403266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:03.470745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.086
86.0%
1.014
 
14.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
286
43.0%
114
 
7.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
286
28.7%
114
 
4.7%

_LTASTH1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
87 
2.0
13 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.087
87.0%
2.013
 
13.0%

Length

2021-06-01T19:13:03.654429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:03.723825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.087
87.0%
2.013
 
13.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
187
29.0%
213
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
187
43.5%
213
 
6.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
187
29.0%
213
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
187
29.0%
213
 
4.3%

_CASTHM1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
90 
2.0
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.090
90.0%
2.010
 
10.0%

Length

2021-06-01T19:13:03.915441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:03.986253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.090
90.0%
2.010
 
10.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
190
30.0%
210
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
190
45.0%
210
 
5.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
190
30.0%
210
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
190
30.0%
210
 
3.3%

_ASTHMS1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
87 
1.0
10 
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.087
87.0%
1.010
 
10.0%
2.03
 
3.0%

Length

2021-06-01T19:13:04.169065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:04.239878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.087
87.0%
1.010
 
10.0%
2.03
 
3.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
387
29.0%
110
 
3.3%
23
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
387
43.5%
110
 
5.0%
23
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
387
29.0%
110
 
3.3%
23
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
387
29.0%
110
 
3.3%
23
 
1.0%

_DRDXAR1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
54 
2.0
46 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Length

2021-06-01T19:13:04.429038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:04.496585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.054
54.0%
2.046
46.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
154
27.0%
246
23.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
154
18.0%
246
15.3%

_EXTETH3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
57 
1.0
34 
9.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row9.0
4th row2.0
5th row9.0

Common Values

ValueCountFrequency (%)
2.057
57.0%
1.034
34.0%
9.09
 
9.0%

Length

2021-06-01T19:13:04.671376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:04.742187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.057
57.0%
1.034
34.0%
9.09
 
9.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
257
19.0%
134
 
11.3%
99
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
257
28.5%
134
 
17.0%
99
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
257
19.0%
134
 
11.3%
99
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
257
19.0%
134
 
11.3%
99
 
3.0%

_DENVST3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
62 
2.0
37 
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.062
62.0%
2.037
37.0%
9.01
 
1.0%

Length

2021-06-01T19:13:04.921535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:04.989778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.062
62.0%
2.037
37.0%
9.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
162
20.7%
237
 
12.3%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
162
31.0%
237
 
18.5%
91
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
162
20.7%
237
 
12.3%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
162
20.7%
237
 
12.3%
91
 
0.3%

_PRACE1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
82 
2.0
13 
6.0
 
4
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.082
82.0%
2.013
 
13.0%
6.04
 
4.0%
3.01
 
1.0%

Length

2021-06-01T19:13:05.179561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:05.249395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.082
82.0%
2.013
 
13.0%
6.04
 
4.0%
3.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
64
 
1.3%
31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
182
41.0%
213
 
6.5%
64
 
2.0%
31
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
64
 
1.3%
31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
64
 
1.3%
31
 
0.3%

_MRACE1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
82 
2.0
13 
6.0
 
3
7.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.082
82.0%
2.013
 
13.0%
6.03
 
3.0%
7.02
 
2.0%

Length

2021-06-01T19:13:05.444881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:05.514144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.082
82.0%
2.013
 
13.0%
6.03
 
3.0%
7.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
63
 
1.0%
72
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
182
41.0%
213
 
6.5%
63
 
1.5%
72
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
63
 
1.0%
72
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
182
27.3%
213
 
4.3%
63
 
1.0%
72
 
0.7%

_HISPANC
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
99 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.099
99.0%
1.01
 
1.0%

Length

2021-06-01T19:13:05.694592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:05.905069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.099
99.0%
1.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
299
33.0%
11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
299
49.5%
11
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
299
33.0%
11
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
299
33.0%
11
 
0.3%

_RACE
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
81 
2.0
13 
6.0
 
3
7.0
 
2
8.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
6.03
 
3.0%
7.02
 
2.0%
8.01
 
1.0%

Length

2021-06-01T19:13:06.084053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:06.152809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
6.03
 
3.0%
7.02
 
2.0%
8.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
63
 
1.0%
72
 
0.7%
81
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
181
40.5%
213
 
6.5%
63
 
1.5%
72
 
1.0%
81
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
63
 
1.0%
72
 
0.7%
81
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
63
 
1.0%
72
 
0.7%
81
 
0.3%

_RACEG21
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
81 
2.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081
81.0%
2.019
 
19.0%

Length

2021-06-01T19:13:06.336585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:06.400608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.081
81.0%
2.019
 
19.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
219
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
181
40.5%
219
 
9.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
219
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
219
 
6.3%

_RACEGR3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
81 
2.0
13 
3.0
 
3
4.0
 
2
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
3.03
 
3.0%
4.02
 
2.0%
5.01
 
1.0%

Length

2021-06-01T19:13:06.593089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:06.667130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
3.03
 
3.0%
4.02
 
2.0%
5.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
33
 
1.0%
42
 
0.7%
51
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
181
40.5%
213
 
6.5%
33
 
1.5%
42
 
1.0%
51
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
33
 
1.0%
42
 
0.7%
51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
33
 
1.0%
42
 
0.7%
51
 
0.3%

_RACE_G1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
81 
2.0
13 
4.0
 
3
5.0
 
2
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
4.03
 
3.0%
5.02
 
2.0%
3.01
 
1.0%

Length

2021-06-01T19:13:06.884927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:06.963328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.081
81.0%
2.013
 
13.0%
4.03
 
3.0%
5.02
 
2.0%
3.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
43
 
1.0%
52
 
0.7%
31
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
181
40.5%
213
 
6.5%
43
 
1.5%
52
 
1.0%
31
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
43
 
1.0%
52
 
0.7%
31
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
181
27.0%
213
 
4.3%
43
 
1.0%
52
 
0.7%
31
 
0.3%

_AGEG5YR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.88
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:07.057076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5.9
Q18
median10
Q312
95-th percentile13
Maximum13
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.76442041
Coefficient of variation (CV)0.2797996366
Kurtosis-0.4329360869
Mean9.88
Median Absolute Deviation (MAD)2
Skewness-0.6515671241
Sum988
Variance7.642020202
MonotonicityNot monotonic
2021-06-01T19:13:07.163962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1323
23.0%
1215
15.0%
813
13.0%
1013
13.0%
611
11.0%
119
 
9.0%
98
 
8.0%
73
 
3.0%
33
 
3.0%
42
 
2.0%
ValueCountFrequency (%)
33
 
3.0%
42
 
2.0%
611
11.0%
73
 
3.0%
813
13.0%
98
 
8.0%
1013
13.0%
119
 
9.0%
1215
15.0%
1323
23.0%
ValueCountFrequency (%)
1323
23.0%
1215
15.0%
119
 
9.0%
1013
13.0%
98
 
8.0%
813
13.0%
73
 
3.0%
611
11.0%
42
 
2.0%
33
 
3.0%

_AGE65YR
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
60 
1.0
40 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.060
60.0%
1.040
40.0%

Length

2021-06-01T19:13:07.392790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:07.466595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.060
60.0%
1.040
40.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
260
20.0%
140
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
260
30.0%
140
 
20.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
260
20.0%
140
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
260
20.0%
140
 
13.3%

_AGE80
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.26
Minimum33
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:07.546727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile45.6
Q158
median68.5
Q378
95-th percentile80
Maximum80
Range47
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.99853916
Coefficient of variation (CV)0.1961747533
Kurtosis-0.2822165511
Mean66.26
Median Absolute Deviation (MAD)10.5
Skewness-0.7530513593
Sum6626
Variance168.9620202
MonotonicityNot monotonic
2021-06-01T19:13:07.666446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
8023
23.0%
786
 
6.0%
775
 
5.0%
595
 
5.0%
704
 
4.0%
494
 
4.0%
584
 
4.0%
624
 
4.0%
483
 
3.0%
463
 
3.0%
Other values (22)39
39.0%
ValueCountFrequency (%)
332
2.0%
341
 
1.0%
371
 
1.0%
381
 
1.0%
463
3.0%
471
 
1.0%
483
3.0%
494
4.0%
501
 
1.0%
542
2.0%
ValueCountFrequency (%)
8023
23.0%
791
 
1.0%
786
 
6.0%
775
 
5.0%
762
 
2.0%
751
 
1.0%
732
 
2.0%
722
 
2.0%
711
 
1.0%
704
 
4.0%

_AGE_G
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
6.0
60 
5.0
21 
4.0
14 
2.0
 
3
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.0
2nd row2.0
3rd row6.0
4th row6.0
5th row6.0

Common Values

ValueCountFrequency (%)
6.060
60.0%
5.021
 
21.0%
4.014
 
14.0%
2.03
 
3.0%
3.02
 
2.0%

Length

2021-06-01T19:13:07.899190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:07.972450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
6.060
60.0%
5.021
 
21.0%
4.014
 
14.0%
2.03
 
3.0%
3.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
660
20.0%
521
 
7.0%
414
 
4.7%
23
 
1.0%
32
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
660
30.0%
521
 
10.5%
414
 
7.0%
23
 
1.5%
32
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
660
20.0%
521
 
7.0%
414
 
4.7%
23
 
1.0%
32
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
660
20.0%
521
 
7.0%
414
 
4.7%
23
 
1.0%
32
 
0.7%

_BMI5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2866.01
Minimum2066
Maximum4826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:08.073529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2066
5-th percentile2162.8
Q12524
median2732.5
Q33125.75
95-th percentile3808.75
Maximum4826
Range2760
Interquartile range (IQR)601.75

Descriptive statistics

Standard deviation532.254847
Coefficient of variation (CV)0.1857128367
Kurtosis1.879842989
Mean2866.01
Median Absolute Deviation (MAD)295
Skewness1.150019496
Sum286601
Variance283295.2221
MonotonicityNot monotonic
2021-06-01T19:13:08.206317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25823
 
3.0%
33202
 
2.0%
26632
 
2.0%
25242
 
2.0%
29292
 
2.0%
27442
 
2.0%
32982
 
2.0%
25771
 
1.0%
31281
 
1.0%
35241
 
1.0%
Other values (82)82
82.0%
ValueCountFrequency (%)
20661
1.0%
21201
1.0%
21411
1.0%
21461
1.0%
21591
1.0%
21631
1.0%
21771
1.0%
21791
1.0%
22311
1.0%
22491
1.0%
ValueCountFrequency (%)
48261
1.0%
46291
1.0%
41201
1.0%
40691
1.0%
39751
1.0%
38001
1.0%
37591
1.0%
37301
1.0%
35241
1.0%
35021
1.0%

_BMI5CAT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
44 
4.0
33 
2.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row3.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.044
44.0%
4.033
33.0%
2.023
23.0%

Length

2021-06-01T19:13:08.423859image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:08.493273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.044
44.0%
4.033
33.0%
2.023
23.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
344
14.7%
433
 
11.0%
223
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
344
22.0%
433
 
16.5%
223
 
11.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
344
14.7%
433
 
11.0%
223
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
344
14.7%
433
 
11.0%
223
 
7.7%

_RFBMI5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
77 
1.0
23 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.077
77.0%
1.023
 
23.0%

Length

2021-06-01T19:13:08.665052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:08.752857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.077
77.0%
1.023
 
23.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
277
25.7%
123
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
277
38.5%
123
 
11.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
277
25.7%
123
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
277
25.7%
123
 
7.7%

_CHLDCNT
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
84 
2.0
10 
3.0
 
5
5.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.084
84.0%
2.010
 
10.0%
3.05
 
5.0%
5.01
 
1.0%

Length

2021-06-01T19:13:09.090259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:09.159077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.084
84.0%
2.010
 
10.0%
3.05
 
5.0%
5.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
184
28.0%
210
 
3.3%
35
 
1.7%
51
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
184
42.0%
210
 
5.0%
35
 
2.5%
51
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
184
28.0%
210
 
3.3%
35
 
1.7%
51
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
184
28.0%
210
 
3.3%
35
 
1.7%
51
 
0.3%

_EDUCAG
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
3.0
36 
2.0
29 
4.0
22 
1.0
12 
9.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row4.0
2nd row4.0
3rd row2.0
4th row2.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.036
36.0%
2.029
29.0%
4.022
22.0%
1.012
 
12.0%
9.01
 
1.0%

Length

2021-06-01T19:13:09.357599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:09.427450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3.036
36.0%
2.029
29.0%
4.022
22.0%
1.012
 
12.0%
9.01
 
1.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
336
 
12.0%
229
 
9.7%
422
 
7.3%
112
 
4.0%
91
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
336
 
18.0%
229
 
14.5%
422
 
11.0%
112
 
6.0%
91
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
336
 
12.0%
229
 
9.7%
422
 
7.3%
112
 
4.0%
91
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
336
 
12.0%
229
 
9.7%
422
 
7.3%
112
 
4.0%
91
 
0.3%

_INCOMG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.77
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:09.506645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q35
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.589235681
Coefficient of variation (CV)0.5428167047
Kurtosis-0.7695804194
Mean4.77
Median Absolute Deviation (MAD)2
Skewness0.5012533516
Sum477
Variance6.704141414
MonotonicityNot monotonic
2021-06-01T19:13:09.597861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
530
30.0%
922
22.0%
416
16.0%
213
13.0%
310
 
10.0%
19
 
9.0%
ValueCountFrequency (%)
19
 
9.0%
213
13.0%
310
 
10.0%
416
16.0%
530
30.0%
922
22.0%
ValueCountFrequency (%)
922
22.0%
530
30.0%
416
16.0%
310
 
10.0%
213
13.0%
19
 
9.0%

_SMOKER3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
4.0
58 
3.0
33 
1.0
 
5
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.058
58.0%
3.033
33.0%
1.05
 
5.0%
2.04
 
4.0%

Length

2021-06-01T19:13:09.820301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:09.892074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
4.058
58.0%
3.033
33.0%
1.05
 
5.0%
2.04
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
458
19.3%
333
 
11.0%
15
 
1.7%
24
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
458
29.0%
333
 
16.5%
15
 
2.5%
24
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
458
19.3%
333
 
11.0%
15
 
1.7%
24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
458
19.3%
333
 
11.0%
15
 
1.7%
24
 
1.3%

_RFSMOK3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
91 
2.0
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.091
91.0%
2.09
 
9.0%

Length

2021-06-01T19:13:10.067739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:10.136188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.091
91.0%
2.09
 
9.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
191
30.3%
29
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
191
45.5%
29
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
191
30.3%
29
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
191
30.3%
29
 
3.0%

DRNKANY5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
66 
1.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.066
66.0%
1.034
34.0%

Length

2021-06-01T19:13:10.309249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:10.376947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.066
66.0%
1.034
34.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
266
22.0%
134
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
266
33.0%
134
 
17.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
266
22.0%
134
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
266
22.0%
134
 
11.3%

DROCDY3_
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.62
Minimum5.4 × 10-79
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:10.443739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.4 × 10-79
5-th percentile5.4 × 10-79
Q15.4 × 10-79
median5.4 × 10-79
Q33
95-th percentile67
Maximum100
Range100
Interquartile range (IQR)3

Descriptive statistics

Standard deviation22.25734699
Coefficient of variation (CV)2.582058815
Kurtosis10.25180044
Mean8.62
Median Absolute Deviation (MAD)0
Skewness3.271437138
Sum862
Variance495.3894949
MonotonicityNot monotonic
2021-06-01T19:13:10.544842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5.4 × 10-7966
66.0%
311
 
11.0%
76
 
6.0%
1004
 
4.0%
333
 
3.0%
143
 
3.0%
672
 
2.0%
291
 
1.0%
431
 
1.0%
171
 
1.0%
Other values (2)2
 
2.0%
ValueCountFrequency (%)
5.4 × 10-7966
66.0%
311
 
11.0%
76
 
6.0%
101
 
1.0%
131
 
1.0%
143
 
3.0%
171
 
1.0%
291
 
1.0%
333
 
3.0%
431
 
1.0%
ValueCountFrequency (%)
1004
4.0%
672
 
2.0%
431
 
1.0%
333
3.0%
291
 
1.0%
171
 
1.0%
143
3.0%
131
 
1.0%
101
 
1.0%
76
6.0%

_RFBING5
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
96 
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.096
96.0%
2.04
 
4.0%

Length

2021-06-01T19:13:10.749496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:10.823331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.096
96.0%
2.04
 
4.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
196
32.0%
24
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
196
48.0%
24
 
2.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
196
32.0%
24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
196
32.0%
24
 
1.3%

_DRNKWEK
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3127.92
Minimum5.4 × 10-79
Maximum99900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2021-06-01T19:13:10.895417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.4 × 10-79
5-th percentile5.4 × 10-79
Q15.4 × 10-79
median5.4 × 10-79
Q329
95-th percentile991.35
Maximum99900
Range99900
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17111.54323
Coefficient of variation (CV)5.470582123
Kurtosis29.84262253
Mean3127.92
Median Absolute Deviation (MAD)0
Skewness5.587330995
Sum312792
Variance292804911.8
MonotonicityNot monotonic
2021-06-01T19:13:10.999828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5.4 × 10-7966
66.0%
239
 
9.0%
934
 
4.0%
7003
 
3.0%
999003
 
3.0%
1402
 
2.0%
9332
 
2.0%
1002
 
2.0%
2331
 
1.0%
1171
 
1.0%
Other values (7)7
 
7.0%
ValueCountFrequency (%)
5.4 × 10-7966
66.0%
239
 
9.0%
471
 
1.0%
701
 
1.0%
934
 
4.0%
1002
 
2.0%
1171
 
1.0%
1402
 
2.0%
2331
 
1.0%
3001
 
1.0%
ValueCountFrequency (%)
999003
3.0%
42001
 
1.0%
21001
 
1.0%
9332
2.0%
7003
3.0%
6001
 
1.0%
4001
 
1.0%
3001
 
1.0%
2331
 
1.0%
1402
2.0%

_RFDRHV6
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
94 
9.0
 
3
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.094
94.0%
9.03
 
3.0%
2.03
 
3.0%

Length

2021-06-01T19:13:11.205494image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:11.282279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.094
94.0%
2.03
 
3.0%
9.03
 
3.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
23
 
1.0%
93
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
194
47.0%
23
 
1.5%
93
 
1.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
23
 
1.0%
93
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
23
 
1.0%
93
 
1.0%

_RFSEAT2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
95 
2.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.095
95.0%
2.05
 
5.0%

Length

2021-06-01T19:13:11.468712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:11.540563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.095
95.0%
2.05
 
5.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
195
31.7%
25
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
195
47.5%
25
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
195
31.7%
25
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
195
31.7%
25
 
1.7%

_RFSEAT3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
1.0
94 
2.0
 
6

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.094
94.0%
2.06
 
6.0%

Length

2021-06-01T19:13:11.714065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:11.784920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.094
94.0%
2.06
 
6.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
26
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
194
47.0%
26
 
3.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
26
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
194
31.3%
26
 
2.0%

_DRNKDRV
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
9.0
66 
2.0
34 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9.0
2nd row2.0
3rd row9.0
4th row9.0
5th row9.0

Common Values

ValueCountFrequency (%)
9.066
66.0%
2.034
34.0%

Length

2021-06-01T19:13:11.971593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:12.043373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
9.066
66.0%
2.034
34.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
966
22.0%
234
 
11.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
966
33.0%
234
 
17.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
966
22.0%
234
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
966
22.0%
234
 
11.3%

_AIDTST3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2.0
81 
1.0
17 
9.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.081
81.0%
1.017
 
17.0%
9.02
 
2.0%

Length

2021-06-01T19:13:12.383546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T19:13:12.451333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2.081
81.0%
1.017
 
17.0%
9.02
 
2.0%

Most occurring characters

ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
92
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number200
66.7%
Other Punctuation100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0100
50.0%
281
40.5%
117
 
8.5%
92
 
1.0%
Other Punctuation
ValueCountFrequency (%)
.100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common300
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
92
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.100
33.3%
0100
33.3%
281
27.0%
117
 
5.7%
92
 
0.7%

Interactions

2021-06-01T19:12:17.967037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.084951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.183721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.288792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.394259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.500121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.601835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.702486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.799733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.902875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:18.999907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.094888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.193213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.294899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.390651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.489422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.588280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.684326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.786283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.887979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:19.990663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.091131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.193469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.287322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.395557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.489422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.584777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.683508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.786235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.882875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:20.993580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.101707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.210454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.320161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.432215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.544440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.792361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:21.899070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.000307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.111011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.209316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.309461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.413182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.521644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.628326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.735190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.842410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:22.947877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.058559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.174281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.284865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.395075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.505039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.615917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.729678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.832926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:23.939865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.046615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.159400image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.265076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.371760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.478239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.584955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.698590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.809834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:24.919539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.029247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.135929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.243641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.354844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.594171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.689913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.792257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:25.902535image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.008405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.113890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.214702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.318485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.428228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.537667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.647143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.752651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.856753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:26.958776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.070934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.169166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.273015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.377316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.485600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.590280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.694144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.796869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:27.900592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.012326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.118047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.227297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.332018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.438820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.544548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.653331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.757603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.859359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:28.965079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.073794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.313485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.414154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.504531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.596312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.694060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.794654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.895592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:29.996483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.097068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.192281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.296942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.389396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.482150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.579478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.681171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.775918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.868989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:30.996614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.105184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.216911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.332601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.448299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.559577image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.670246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.776747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.887344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:31.990036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.092795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.198092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.311466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.416989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.525139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.615050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.709113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:32.806363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.039716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.131469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.223370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.315134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.406886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.508165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.595927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.682693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.776062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.871770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:33.963135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.053861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.147547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.240314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.337106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.437745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.534123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.631823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.729196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.821551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:34.926775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.022639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.117275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.215008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.314925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.409705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.502578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.601282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.698079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.803905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:35.913611image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.026369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.137073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.249433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.357145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.480557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.762818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.879018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:36.997324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.118011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.225686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.336692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.448335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.560037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.688694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.826446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:37.996989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.142627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.276271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.406924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.547348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.673562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.791217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:38.911393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.037063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.151752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.268440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.379473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.489180image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.603629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.721177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.833908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:39.947370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.062612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.169616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.281318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.380016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.479944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.604120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:40.815555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.010696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.360420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.506542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.648163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.793802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:41.921524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.046753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.164545image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.285228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.399030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.523724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.640967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.757660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:42.894293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:43.027935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-06-01T19:12:43.150605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-06-01T19:13:12.715141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-01T19:13:14.283275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-01T19:13:15.679869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-01T19:13:17.152022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-01T19:13:18.477125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-01T19:12:43.782233image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-01T19:12:47.717229image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

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object_id
01.02.030.088.01.01.02.01.02.07.02.02.02.02.01.02.02.01.02.02.03.01.01.02.03.06.01.02.02.088.06.02.02.02.01.02.02.02.03.0888.01.01.01.02.02.010.01.02.02.01.01.01.03.01.09.02.02.01.01.03.01.02.01.01.01.02.01.01.01.01.013.02.080.06.02231.02.01.01.04.04.04.01.02.05.400000e-791.05.400000e-791.01.01.09.02.0
11.03.088.088.02.01.01.02.01.05.02.02.02.02.02.02.02.02.02.02.03.02.08.02.05.06.02.02.01.02.04.02.02.02.02.02.02.01.03.0202.02.02.01.02.02.010.01.01.01.02.01.01.01.01.02.01.02.01.01.03.02.01.02.02.02.02.02.02.02.02.03.01.033.02.03328.04.02.03.04.02.01.02.01.07.000000e+001.09.300000e+011.01.01.02.02.0
21.05.010.088.01.01.02.01.01.07.02.02.01.02.02.02.02.02.01.02.01.01.07.02.03.04.01.02.07.088.03.02.02.02.01.02.02.02.03.0888.01.01.01.02.02.010.01.02.01.01.09.02.02.01.09.01.02.01.01.03.02.09.01.01.01.02.01.01.01.01.012.02.076.06.02968.03.02.01.02.02.04.01.02.05.400000e-791.05.400000e-791.01.01.09.02.0
31.01.088.088.01.01.02.01.01.06.02.02.02.02.02.02.02.02.02.02.03.03.01.01.02.04.01.02.07.088.03.02.02.02.02.02.02.02.03.0888.01.01.01.02.02.010.01.01.01.01.09.01.01.01.09.01.02.01.01.03.02.02.02.01.01.02.01.01.01.01.010.02.066.06.02726.03.02.01.02.02.04.01.02.05.400000e-791.05.400000e-791.01.01.09.02.0
41.02.088.088.01.02.02.01.02.06.02.01.02.01.02.02.02.02.02.02.04.01.07.02.03.05.01.02.07.088.099.02.02.02.02.02.02.02.03.0888.01.01.01.02.02.010.01.01.01.01.01.01.01.01.09.02.01.02.02.01.02.09.01.01.01.02.01.01.01.01.013.02.080.06.03146.04.02.01.03.09.04.01.02.05.400000e-791.05.400000e-791.01.01.09.02.0
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81.03.05.088.01.01.02.01.01.07.02.02.02.02.02.02.01.02.02.02.03.01.08.02.02.06.02.02.01.088.08.02.02.02.02.02.02.02.03.0888.01.02.01.01.02.010.01.01.01.02.01.01.02.01.01.01.02.01.01.03.02.01.01.02.02.02.02.02.02.02.06.01.048.04.02675.03.02.01.04.05.04.01.02.05.400000e-791.05.400000e-791.01.01.09.01.0
91.01.088.088.01.02.02.01.02.010.02.02.02.02.02.02.02.02.02.02.03.01.02.01.01.04.01.02.07.088.04.02.02.02.02.02.02.01.03.0888.01.01.01.02.02.010.01.01.01.01.01.01.01.01.09.02.02.01.01.03.02.02.01.01.01.02.01.01.01.01.013.02.080.06.02699.03.02.01.02.02.03.01.02.05.400000e-791.05.400000e-791.01.01.09.02.0

Last rows

_STATEGENHLTHPHYSHLTHMENTHLTHHLTHPLN1PERSDOC2MEDCOSTCHECKUP1EXERANY2SLEPTIM1CVDINFR4CVDCRHD4CVDSTRK3ASTHMA3CHCSCNCRCHCOCNCRCHCCOPD1HAVARTH3ADDEPEV2CHCKDNY1DIABETE3LASTDEN4RMVTETH4SEX1MARITALEDUCARENTHOM1VETERAN3EMPLOY1CHILDRENINCOME2DEAFBLINDDECIDEDIFFWALKDIFFDRESDIFFALONSMOKE100USENOW3ALCDAY5FLUSHOT6PNEUVAC4SEATBELTHIVTST6HIVRISK5QSTVERQSTLANG_METSTAT_URBSTAT_IMPRACE_DUALUSE_RFHLTH_PHYS14D_MENT14D_HCVU651_TOTINDA_MICHD_LTASTH1_CASTHM1_ASTHMS1_DRDXAR1_EXTETH3_DENVST3_PRACE1_MRACE1_HISPANC_RACE_RACEG21_RACEGR3_RACE_G1_AGEG5YR_AGE65YR_AGE80_AGE_G_BMI5_BMI5CAT_RFBMI5_CHLDCNT_EDUCAG_INCOMG_SMOKER3_RFSMOK3DRNKANY5DROCDY3__RFBING5_DRNKWEK_RFDRHV6_RFSEAT2_RFSEAT3_DRNKDRV_AIDTST3
object_id
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